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The gap between a demo agent and a production agent isn't the model. It's everything around it. #AIEngineering

Here's a framing I keep coming back to: Agent = Model + Harness. #AIAgents

The model gets the attention. The harness does the work. #AgenticAI

After building agents that have run in production, I've become convinced the harness matters as much as, or maybe more than, the model itself. What separates a brittle demo from something that holds up under real conditions is the quality of everything built around the model. #SoftwareEngineering

So what does that mean in practice? #SystemDesign

Clean execution environments matter more than people think. Agents need isolated, deterministic contexts to behave reliably. Leftover state, dirty environments, unexpected side effects: these silently corrupt agent behavior in ways that are hard to trace. #Backend

Don't underestimate state management. Most agent failures aren't model failures. They're state coherence failures. The harness controls what the model sees and when, and that decision matters. #SoftwareArchitecture

You can't improve what you can't see. Tracing, logging, replay: this is where the harness earns its keep. Observability isn't a nice-to-have. It's how you actually get better. #Observability

A strong model in a weak harness will consistently underperform a weaker model in a strong one. #Reliability